@inproceedings{9bc3b9802acf411bb3ec1b188a9b4df7,
title = "KnowReQA: A Knowledge-aware Retrieval Question Answering System",
abstract = "Retrieval question answering (ReQA) is an essential mechanism to automatically satisfy the users{\textquoteright} information needs and overcome the problem of information overload. As a promising solution to achieve fast retrieval from large-scale candidate answers, dual-encoder framework has been widely studied to improve its representation quality for text in the recent years. Inspired by that humans usually answer the question using their background knowledge, in this work, we explore the way to incorporate knowledge entities into the retrieval model to build high-quality text representations and propose novel knowledge-aware text encoding and knowledge-aware text matching modules to facilitate the fusion between text and knowledge. The promising experimental results on various benchmarks prove the potential of the proposed approach.",
keywords = "Dual-encoder, Knowledge aware retrieval, Natural language processing, Retrieval question answering",
author = "Chuanrui Wang and Jun Bai and Xiaofeng Zhang and Cen Yan and Yuanxin Ouyang and Wenge Rong and Zhang Xiong",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Knowledge Science, Engineering and Management, KSEM 2022 ; Conference date: 06-08-2022 Through 08-08-2022",
year = "2022",
doi = "10.1007/978-3-031-10983-6\_54",
language = "英语",
isbn = "9783031109829",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "709--721",
editor = "Gerard Memmi and Baijian Yang and Linghe Kong and Tianwei Zhang and Meikang Qiu",
booktitle = "Knowledge Science, Engineering and Management - 15th International Conference, KSEM 2022, Proceedings",
address = "德国",
}